Job Summary
We are seeking a highly skilled and detail-oriented Data Engineer to join our growing analytics and engineering team in Toronto. The successful candidate will be responsible for building and maintaining scalable data pipelines, optimizing data architecture, and enabling reliable data access for analytics, reporting, and machine learning.
Key Responsibilities
Design, develop, and maintain robust data pipelines to collect, process, and transform structured and unstructured data from multiple sources.
Build and optimize data architectures that support analytics, BI, and machine learning workloads.
Develop and maintain ETL/ELT processes using tools such as Apache Airflow , AWS Glue , Azure Data Factory , or dbt .
Collaborate with data analysts and scientists to design and implement data models and schemas for analytics.
Manage and optimize data lakes and data warehouses (e.g., Snowflake, BigQuery, Redshift, Synapse).
Ensure data quality, validation, and integrity through automated checks and monitoring systems.
Implement and enforce data governance, lineage, and security best practices .
Work with DevOps and cloud engineering teams to automate deployment of data infrastructure.
Develop scripts and tools for data migration, cleaning, and transformation .
Monitor data workflows for performance, cost, and reliability improvements.
Stay updated with the latest trends in data engineering, big data, and cloud analytics technologies.
Educational Qualifications
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related field .
Strong proficiency in SQL and experience with data modeling and schema design .
Hands-on experience with Python (or Scala/Java) for data processing and automation.
Proficiency in cloud data platforms such as AWS (Redshift, Glue, S3) , Azure (Synapse, Data Lake, Data Factory) , or GCP (BigQuery, Dataflow) .
Experience with ETL/ELT tools (Airflow, dbt, Talend, Informatica).
Knowledge of data warehousing concepts , data lakes , and dimensional modeling .
Familiarity with big data frameworks (Apache Spark, Hadoop, Kafka).
Experience with version control (Git) and CI/CD pipelines for data workflows.
Strong understanding of data governance, security, and compliance (GDPR, PII, etc.).
Excellent analytical, problem-solving, and communication skills.
Preferred Qualifications
Experience with Snowflake , Databricks , or Delta Lake .
Familiarity with real-time data streaming (Kafka, Kinesis, Pub/Sub).
Knowledge of data quality frameworks such as Great Expectations or Monte Carlo .
Experience with MLOps and integrating data pipelines with ML workflows.
Exposure to containerization (Docker, Kubernetes) and IaC tools (Terraform) .
Certifications such as:
AWS Certified Data Analytics Specialty
Microsoft Certified: Azure Data Engineer Associate
Google Professional Data Engineer
Experience working in FinTech, Healthcare, or SaaS data-driven environments.
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